The DOI matches its Crossref record; 18 of the 19 references it cites were found in a registry or an index.
Tools for privacy preserving distributed data mining
Abstract
Privacy preserving mining of distributed data has numerous applications. Each application poses different constraints: What is meant by privacy, what are the desired results, how is the data distributed, what are the constraints on collaboration and cooperative computing, etc. We suggest that the solution to this is a toolkit of components that can be combined for specific privacy-preserving data mining applications. This paper presents some components of such a toolkit, and shows how they can be used to solve several privacy-preserving data mining problems.
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“Clifton et al. [117] describe a toolkit for various privacypreserving SMC-based data mining operations including secure sum computation using a secret random number.”
From A Survey of Self-Sovereign Identity Ecosystem (2021) · cites this paper as [117]
In the citing paper
“However, in this protocol, two adjacent parties can collude to compute the random number.”
From A Survey of Self-Sovereign Identity Ecosystem (2021) · cites this paper as [117]
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Metadata
- Authors
- Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., & Zhu, M. Y.
- Affiliations
- Purdue University West Lafayette (US)
- Purdue University, West Lafayette, IN
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2002-12
- Volume
- 4
- Issue
- 2
- Pages
- 28-34
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2002352982
- Cited by
- 890
- References
- 19
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 12 · 2023: 14 · 2022: 27 · 2021: 38 · 2020: 31 · 2019: 40 · 2018: 46 · 2017: 41 · 2016: 51 · 2015: 57 · 2014: 70 · 2013: 68 · 2012: 50
- FWCI
- 14.20
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Explorations Newsletter (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- privacy-preserving data mining, distributed data mining, privacy definitions, collaborative computing, privacy-preserving association rule mining, privacy-preserving clustering
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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